480
Hengl T et al (2017) SoilGrids250m: global gridded soil information based on machine learning.
PLoS One 12(2):e0169748. https://doi.org/10.1371/journal.pone.0169748
Hobi ML, Dubinin M, Graham CH, Coops NC, Clayton MK, Pidgeon AM, Radeloff VC (2017) A
comparison of dynamic habitat indices derived from different MODIS products as predictors
of avian species richness. Remote Sens Environ 194:142–152
Hostetler M, Allen W, Meurk C (2011) Conserving urban biodiversity? Creating green infrastructure is only the first step. Landsc Urban Plan 100:369–371
Hui C, Richardson DM, Visser V (2017) Ranking of invasive spread through urban green areas in
the world’s 100 most populous cities. Biol Invasions 19:3527–3539
IPBES (2016) In: Potts SG, Imperatriz-Fonseca VL, Ngo HT (eds) The assessment report of the
intergovernmental science-policy platform on biodiversity and ecosystem services on pollinators, pollination and food production. Secretariat of the Intergovernmental Science-Policy
Platform on Biodiversity and Ecosystem Services, Bonn, p 552
INEGI (2013) National Institute for Statistics and Geography Mexico Land Cover Map. https://
www.inegi.org.mx/
Iversen C et al (2018) Fine-root ecology database FRED: a global collection of root trait data
with coincident site, vegetation, edaphic, and climatic data, version 2, Oak Ridge National
Laboratory, TES SFA, U.S. Department of Energy, Oak Ridge. Available at: https://doi.
org/10.25581/ornlsfa.012/1417481
Jetz W, Cavender-Bares J, Pavlick R, Schimel D, Davis FW, Asner GP, Guralnick R, Kattge J,
Latimer AM, Moorcroft P, Schaepman ME, Schildhauer MP, Schneider FD, Schrodt F, Stahl U,
Ustin SL (2016) Monitoring plant functional diversity from space. Nat Plants 2
Jones HG, Vaughan RA (2010) Remote sensing of vegetation: principles, techniques and applications. Oxford University Press, Oxford
Joshi N et al (2016) A review of the application of optical and radar remote sensing data fusion to
land use mapping and monitoring. Remote Sens 81:1–23. https://doi.org/10.3390/rs8010070
Justice C, Roman M, Csiszar I, Vermonte E, Wolfe R, Hook S, Friedl M, Wang Z, Schaaf C, Miura
T, Tschudi M, Riggs F, Hall D, Lyapustin A, Devadiga S, Davidson C, Masuoka E (2013) Land
and cryosphere products from Suomi NPP VIIRS: overview and status. J Geophys Res Atmos
118:9753–9765
Karger DN, Conrad O, Böhner J, Kawohl T, Kreft H, Soria-Auza RW, Zimmermann NE, Linder
HP, Kessler M (2017) Climatologies at high resolution for the earth’s land surface areas. Sci
Data 4:170122
Kattge J et al (2011) TRY – a global database of plant traits. Glob Chang Biol 17:2905–2935.
https://doi.org/10.1111/j.1365-2486.2011.02451.x
Keenan TF, Niinemets Ü (2016) Global leaf trait estimates biased due to plasticity in the shade,
Nature Plants. Nat Publ Group 3:16201. https://doi.org/10.1038/nplants.2016.201
Kennedy RE, Andrefouet S, Cohen WB, Gomez C, Griffiths P, Hais M et al (2014) Bringing an
ecological view of change to Landsat-based remote sensing. Front Ecol Environ 126:339–346.
https://doi.org/10.1890/130066
Kerr J, Ostrovsky M (2003) From space to species: ecological applications for remote sensing.
Trends Ecol Evol 18:299–305
Khormi HM, Kumar L (2011) Modeling dengue fever risk based on socioeconomic parameters,
nationality and age groups: GIS and remote sensing based case study. Sci Total Environ
409:4713–4719
Khorram S, van der Wiele CF, Koch FH, Nelson SAC, Potts MD (2016) Principles of applied
remote sensing, pp 21–31
Koltunov A, Ustin SL, Asner GP, Fung I (2009) Selective logging changes forest phenology in the
Brazilian Amazon: evidence from MODIS images time series analysis. Remote Sens Environ
113:2431–2440
Kosmala M et al (2016) Season spotter: using citizen science to validate and scale plant phenology
from near-surface remote sensing. Remote Sens 89:1–22. https://doi.org/10.3390/rs8090726
Kramer-Schadt S et al (2013) The importance of correcting for sampling bias in MaxEnt species
distribution models. Divers Distrib 19:1366–1379. https://doi.org/10.1111/ddi.12096
F. Schrodt et al.
Hengl T et al (2017) SoilGrids250m: global gridded soil information based on machine learning.
PLoS One 12(2):e0169748. https://doi.org/10.1371/journal.pone.0169748
Hobi ML, Dubinin M, Graham CH, Coops NC, Clayton MK, Pidgeon AM, Radeloff VC (2017) A
comparison of dynamic habitat indices derived from different MODIS products as predictors
of avian species richness. Remote Sens Environ 194:142–152
Hostetler M, Allen W, Meurk C (2011) Conserving urban biodiversity? Creating green infrastructure is only the first step. Landsc Urban Plan 100:369–371
Hui C, Richardson DM, Visser V (2017) Ranking of invasive spread through urban green areas in
the world’s 100 most populous cities. Biol Invasions 19:3527–3539
IPBES (2016) In: Potts SG, Imperatriz-Fonseca VL, Ngo HT (eds) The assessment report of the
intergovernmental science-policy platform on biodiversity and ecosystem services on pollinators, pollination and food production. Secretariat of the Intergovernmental Science-Policy
Platform on Biodiversity and Ecosystem Services, Bonn, p 552
INEGI (2013) National Institute for Statistics and Geography Mexico Land Cover Map. https://
www.inegi.org.mx/
Iversen C et al (2018) Fine-root ecology database FRED: a global collection of root trait data
with coincident site, vegetation, edaphic, and climatic data, version 2, Oak Ridge National
Laboratory, TES SFA, U.S. Department of Energy, Oak Ridge. Available at: https://doi.
org/10.25581/ornlsfa.012/1417481
Jetz W, Cavender-Bares J, Pavlick R, Schimel D, Davis FW, Asner GP, Guralnick R, Kattge J,
Latimer AM, Moorcroft P, Schaepman ME, Schildhauer MP, Schneider FD, Schrodt F, Stahl U,
Ustin SL (2016) Monitoring plant functional diversity from space. Nat Plants 2
Jones HG, Vaughan RA (2010) Remote sensing of vegetation: principles, techniques and applications. Oxford University Press, Oxford
Joshi N et al (2016) A review of the application of optical and radar remote sensing data fusion to
land use mapping and monitoring. Remote Sens 81:1–23. https://doi.org/10.3390/rs8010070
Justice C, Roman M, Csiszar I, Vermonte E, Wolfe R, Hook S, Friedl M, Wang Z, Schaaf C, Miura
T, Tschudi M, Riggs F, Hall D, Lyapustin A, Devadiga S, Davidson C, Masuoka E (2013) Land
and cryosphere products from Suomi NPP VIIRS: overview and status. J Geophys Res Atmos
118:9753–9765
Karger DN, Conrad O, Böhner J, Kawohl T, Kreft H, Soria-Auza RW, Zimmermann NE, Linder
HP, Kessler M (2017) Climatologies at high resolution for the earth’s land surface areas. Sci
Data 4:170122
Kattge J et al (2011) TRY – a global database of plant traits. Glob Chang Biol 17:2905–2935.
https://doi.org/10.1111/j.1365-2486.2011.02451.x
Keenan TF, Niinemets Ü (2016) Global leaf trait estimates biased due to plasticity in the shade,
Nature Plants. Nat Publ Group 3:16201. https://doi.org/10.1038/nplants.2016.201
Kennedy RE, Andrefouet S, Cohen WB, Gomez C, Griffiths P, Hais M et al (2014) Bringing an
ecological view of change to Landsat-based remote sensing. Front Ecol Environ 126:339–346.
https://doi.org/10.1890/130066
Kerr J, Ostrovsky M (2003) From space to species: ecological applications for remote sensing.
Trends Ecol Evol 18:299–305
Khormi HM, Kumar L (2011) Modeling dengue fever risk based on socioeconomic parameters,
nationality and age groups: GIS and remote sensing based case study. Sci Total Environ
409:4713–4719
Khorram S, van der Wiele CF, Koch FH, Nelson SAC, Potts MD (2016) Principles of applied
remote sensing, pp 21–31
Koltunov A, Ustin SL, Asner GP, Fung I (2009) Selective logging changes forest phenology in the
Brazilian Amazon: evidence from MODIS images time series analysis. Remote Sens Environ
113:2431–2440
Kosmala M et al (2016) Season spotter: using citizen science to validate and scale plant phenology
from near-surface remote sensing. Remote Sens 89:1–22. https://doi.org/10.3390/rs8090726
Kramer-Schadt S et al (2013) The importance of correcting for sampling bias in MaxEnt species
distribution models. Divers Distrib 19:1366–1379. https://doi.org/10.1111/ddi.12096
F. Schrodt et al.